// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::Tensor;
template<typename DataType, int DataLayout, typename IndexType>
void
test_forced_eval_sycl(const Eigen::SyclDevice& sycl_device)
{

	IndexType sizeDim1 = 100;
	IndexType sizeDim2 = 20;
	IndexType sizeDim3 = 20;
	Eigen::array<IndexType, 3> tensorRange = { { sizeDim1, sizeDim2, sizeDim3 } };
	Eigen::Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
	Eigen::Tensor<DataType, 3, DataLayout, IndexType> in2(tensorRange);
	Eigen::Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);

	DataType* gpu_in1_data =
		static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize() * sizeof(DataType)));
	DataType* gpu_in2_data =
		static_cast<DataType*>(sycl_device.allocate(in2.dimensions().TotalSize() * sizeof(DataType)));
	DataType* gpu_out_data =
		static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(DataType)));

	in1 = in1.random() + in1.constant(static_cast<DataType>(10.0f));
	in2 = in2.random() + in2.constant(static_cast<DataType>(10.0f));

	// creating TensorMap from tensor
	Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
	Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
	Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
	sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(), (in1.dimensions().TotalSize()) * sizeof(DataType));
	sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(), (in2.dimensions().TotalSize()) * sizeof(DataType));
	/// c=(a+b)*b
	gpu_out.device(sycl_device) = (gpu_in1 + gpu_in2).eval() * gpu_in2;
	sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(DataType));
	for (IndexType i = 0; i < sizeDim1; ++i) {
		for (IndexType j = 0; j < sizeDim2; ++j) {
			for (IndexType k = 0; k < sizeDim3; ++k) {
				VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) + in2(i, j, k)) * in2(i, j, k));
			}
		}
	}
	printf("(a+b)*b Test Passed\n");
	sycl_device.deallocate(gpu_in1_data);
	sycl_device.deallocate(gpu_in2_data);
	sycl_device.deallocate(gpu_out_data);
}

template<typename DataType, typename Dev_selector>
void
tensorForced_evalperDevice(Dev_selector s)
{
	QueueInterface queueInterface(s);
	auto sycl_device = Eigen::SyclDevice(&queueInterface);
	test_forced_eval_sycl<DataType, RowMajor, int64_t>(sycl_device);
	test_forced_eval_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_forced_eval_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		CALL_SUBTEST(tensorForced_evalperDevice<float>(device));
		CALL_SUBTEST(tensorForced_evalperDevice<half>(device));
	}
}
